Databases Reference
In-Depth Information
Being an Ethical Data Scientist
You all are not just nerds sitting in the corner. You have increasingly
important ethical questions to consider while you work.
We now have tons of data on market and human behavior. As data
scientists, we bring not just a set of machine learning tools, but also
our humanity, to interpret and find meaning in data and make ethical,
data-driven decisions.
Keep in mind that the data generated by user behavior becomes the
building blocks of data products, which simultaneously are used
by
users and
influence
user behavior. We see this in recommendation
systems, ranking algorithms, friend suggestions, etc., and we will see
it increasingly across sectors including education, finance, retail, and
health. Things can go wrong with such feedback loops: keep the fi‐
nancial meltdown in mind as a cautionary example.
Much is made about predicting the future (see
Nate Silver
)
, predicting
the present (see
Hal Varian
), and exploring causal relationships from
observed data (the past; see
Sinan Aral
)
.
The next logical concept then is: models and algorithms are not only
capable of predicting the future, but also of causing the future. That's
what we can look forward to, in the best of cases, and what we should
fear in the worst.
As an introduction to how to approach these issues ethically, let's start
with Emanuel Derman's
Hippocratic Oath of Modeling
, which was
made for financial modeling but fits perfectly into this framework:
• I will remember that I didn't make the world and that it doesn't
satisfy my equations.
• Though I will use models boldly to estimate value, I will not be
overly impressed by mathematics.
• I will never sacrifice reality for elegance without explaining why
I have done so. Nor will I give the people who use my model false
comfort about its accuracy. Instead, I will make explicit its as‐
sumptions and oversights.
• I understand that my work may have enormous effects on society
and the economy, many of them beyond my comprehension.
